Sleep monitoring system

ABSTRACT

A sleep monitoring system includes an ECG device and a respiration inductance plethysmogram which monitor cardiac activity and physical respiration respectively and feed representative signals to a digital data processor. Operations process the beat interval data, while in a second thread, operations independently process the amplitude modulation of the ECG data caused by the respiratory motion of the subject. The inductance plethysmogram device provides an input to the processor which represents respiration as directly monitored independently of the ECG. Operations process this direct respiration data independently and in parallel, in a third thread. All extracted features are fed to a classifier which in step combines selected combinations of features to make decisions in real time.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a divisional of U.S. patent application Ser.No. 11/718,301, filed on Feb. 2, 2009, which application is a nationalphase entry under 35 U.S.C. § 371 of International Application No.PCT/IE2005/000122 filed Nov. 2, 2005, which claims priority from IrishPatent Application No. 2004/0731 filed Nov. 2, 2004, all of which arehereby incorporated herein by reference.

FIELD OF THE INVENTION

The invention relates to monitoring of a person's sleep pattern.

PRIOR ART DISCUSSION

It is known to provide a system to receive and process signals fromsensors in order to monitor a person's sleep pattern. In one approachsleep stages are determined using signals from a polysonmogram system,in which the sleep staging component is based on measuringelectroencephalograms (EEG) which are a direct measurement of brainactivity. This approach has a number of disadvantages. First of all,polysonmogram monitoring equipment is complex and generally needs to beoperated and analyzed in a clinic by skilled technicians. The patient isrequired to visit a clinic for an overnight study where skilledtechnicians attach the electrodes to the head, chest, chin and leg,together with a chest band and an airflow monitor. This is a costly andtime-consuming process. If the polysonmogram system is operated by apatient at home, there is the requirement that the electrodes areattached correctly, and in particular that the EEG electrodes arecorrectly placed and attach, or otherwise the extremely low voltage EEGsignals will not be recorded correctly. Furthermore, the use of a numberof electrodes attached to the head during sleep is uncomfortable anddisrupts the patient's sleep.

In another approach, motion based systems (actimetry) are used. However,such systems have the disadvantage that they can only distinguishbetween sleep and wake, with poor accuracy in patients with sleepdisorders.

U.S. Pat. No. 5,280,791 describes an approach in which cardiac R-R waveintervals are used to designate sleep as REM or non-REM. A powerspectrum of the cardiac R-R interval is calculated.

The prior art systems do not appear to analyse specific sleep stagessufficiently to recognise periods of wakefulness. In addition, wherestages such as REM and non-REM are differentiated it appears that theperformance is quite poor as the decision is based on comparison of asingle parameter with a previously determined threshold value.

Therefore the current state of the art in determining sleep stages islimited by (a) the need to directly measure brain activity, and (b) poorperformance when using observations of single parameters of cardiacactivity.

SUMMARY OF THE INVENTION

According to the invention there is provided a sleep monitoring systemcomprising:

-   -   an interface for receiving sensor signals;    -   a processor for extracting a plurality of features from the        sensor signals; and    -   a classifier for generating an output indicating sleep stage        according to classification of the features.

In one embodiment, the processor extracts time domain and frequencydomain features.

In one embodiment, the processor measures heartbeat intervals fromcardiogram sensor signals and uses said measurements to extractfeatures.

In one embodiment, the extracted features include mean interval perepoch, standard deviation of intervals, longest interval, and shortestinterval.

In another embodiment, the processor measures amplitude modulation ofthe cardiogram sensor signals caused by respiratory motion of a personand uses said measurement to extract features.

In one embodiment, the extracted features include variance of a derivedrespiratory signal, and power of the respiratory signal at a frequencyband.

In one embodiment, the extracted features include the dominant frequencyof respiration and the power at the dominant respiratory frequency.

In one embodiment, the processor independently extracts features fromthe heartbeat interval measurements and from the amplitude modulationmeasurements.

In a further embodiment, the interface receives sensor signals from adevice for physically monitoring patient respiration.

In one embodiment, the processor measures variations in signals fromsaid device.

In one embodiment, the processor uses said measurements to independentlyextract features.

In one embodiment, the features extracted by the processor from thesignals from said device include ribcage respiratory effort in each of aplurality of frequency bands, envelope power, and breath lengthvariation.

In one embodiment, the interface receives sensor signals from devicessimultaneously monitoring patient respiration and patient cardiograms,and the processor simultaneously processes said signals.

In one embodiment, said device comprises an inductance plethysmograph.

In one embodiment, the processor uses measurements from said sensorsignals to extract features independently from extraction of featuresfrom cardiogram sensor signals.

In one embodiment, the features are extracted for each of a series ofepochs.

In one embodiment, the epochs have a duration of less than 30 seconds.

In one embodiment, the processor extracts detrended features derivedfrom a plurality of epochs in sequence.

In one embodiment, the detrended features are generated by subtracting alocal mean signal from epoch feature values.

In one embodiment, the classifier operates according to a discriminantclassifier model.

In one embodiment, the classifier comprises a search process foridentifying a subset of the features to use for optimum classificationperformance.

In one embodiment, the search process performs a sequential forwardfloating search, in which a coefficient is a measure of an inter-rateragreement taking account of a prior probability of a specific classoccurring.

In one embodiment, said process executes passes which add a feature thatmost improves performance to already-selected features.

In another aspect, the invention provides a sleep monitoring methodperformed by a system comprising a sensor interface and a processor, themethod comprising the steps of:

-   -   the interface receiving signals from at least one sensor        monitoring a patient while asleep;    -   the processor extracting a plurality of features from the sensor        signals; and    -   the processor, operating as a classifier, generating an output        indicating sleep stage according to classification of the        features.

DETAILED DESCRIPTION OF THE INVENTION Brief Description of the Drawings

The invention will be more clearly understood from the followingdescription of some embodiments thereof, given by way of example onlywith reference to the accompanying drawings in which:

FIG. 1 is a flow diagram illustrating operation of a sleep monitoringsystem of the invention;

FIGS. 2A and 2B are plots of ECG and respiration inductanceplethysmograph signals (sensor data) showing parameters extracted suchas interbeat interval, QRS amplitude, and interbreath interval;

FIG. 3 is a plot of the possible outputs from the sleep staging system,in which the upper panel represents the stages of a person's sleep overone night, broken into periods of Wake, Non-REM Sleep, and Sleep; and

FIG. 4 is a plot illustrating how features from different sleep stagescan form different clusters in the classification space.

DESCRIPTION OF THE EMBODIMENTS

Referring to FIG. 1 a system of the invention includes an ECG device 2and a respiration inductance plethysmogram 3 which monitor cardiacactivity and physical (ribcage) respiration respectively and feedrepresentative signals to a digital data processor. The processor isprogrammed to implement operations 5-10, 20-24, and 30-34 of FIG. 1 togenerate an indication of the current sleep stage of a patient.Alternative devices for measurement of respiration may also be employedsuch as impedance pneumograms or air-flow tachometers.

Referring also to FIGS. 2A and 2B the electrocardiogram (ECG) device 2provides a processor input signal containing information concerning theheart beat intervals and also respiration. The beat intervals aredetermined by directly measuring the time intervals between peaks in theECG input. In the example of FIG. 2A these are 0.968 s, 0.984 s, 0.953s, and 0.937 s.

A first set of respiration information is derived indirectly bymonitoring the modulation of the amplitude of the ECG signal provided bythe device 2 which is caused by the respiration pattern.

The operations 5-9 process the beat interval data, while in a secondthread, the operations 20-24 independently process the amplitudemodulation of the ECG data caused by the respiratory motion of thesubject.

The inductance plethysmogram device 3 provides an input to the processorwhich represents respiration as directly monitored independently of theECG. An example of this input is shown in FIG. 2B. The operations 30-34process this direct respiration data independently and in parallel, in athird thread.

All three strands independently extract features. Because the operations20-24 and 30-34 both process respiration data the features they extractshould in theory be the same. However, in practice they are typicallydifferent and the system benefits from having both.

All extracted features are fed to a classifier which in step 10 combinesselected combinations of features as shown in FIG. 4 to make decisionsin real time. Because many extracted features are available to theclassifier it has a rich body of knowledge available to it. Thisprovides the benefit of:

-   -   (a) very accurate and consistent determination of the current        sleep stage,    -   (b) excellent versatility because a wide range of sleep stages        and sub-stages can be defined by appropriate training of the        classifier, even stages with relatively little difference        between them, and    -   (c) a high level of system robustness since loss of data from        one sensor (either ECG or respiration) can be compensated for by        employing data from the other.

Referring again to FIG. 1, in step 5 the processor measures intervals inthe ECG input for an epoch of, say, 30 seconds. In step 6, it uses thisdata to extract frequency domain features such as power in differentbands, power at specific frequencies, and ratio of power and bands.

In step 7 it extracts time domain features such as mean interval perepoch, standard deviation of intervals, longest interval, and shortestinterval. It is also advantageous to extract intrabeat interval, such asthe QT or PR interval for each beat, as these are also indicative of theunderlying physiological state of the subject.

As indicated by the decision step 8 these features are extracted foreach of a series of epochs in a time period such as 15 mins. In step 9the processor extracts “detrended” time domain features across all ofthe epochs for a time period. A “detrended” feature is one in which anew signal is formed by subtracting off the local mean signal.

For the ECG-derived respiration data the processor in step 20 measuresthe amplitude modulation caused by the sleep respiration. In steps 21and 22 frequency domain and time domain features are extracted. Theseinclude the overall variance of the derived respiratory signal, thepower of the respiratory signal at various frequency bands, the dominantfrequency of respiration (e.g., 16 breaths per minute), and the power atthe dominant respiratory frequency (which reflects the amplitude ofrespiration).

As shown by the decision step 23, these are repeated for each epoch insuccession.

The operations 30-34 use the direct respiration signal to measure theamplitude modulation in step 30, and extract frequency domain and timedomain features in steps 31 and 32. As indicated by the decision step 33these features are extracted for each epoch in turn. Detrended featuresare extracted in step 34. The features for this process are the same asthose for the process 20-24.

The classifier is initially trained using brain activity inputs 50 asthese are the most representative of sleep stages, and provide anaccurate baseline to train the classifier. Thus, the system of theinvention can achieve the accuracy of a system which uses brain activityinputs without the need for the inconvenience of brain activitymonitoring sensors. Moreover, the system can be readily applied to newsubjects since the training across previous subjects with known sleepstages has established the correct decision parameters for the system.The classifier can also be preferentially trained to optimiseperformance on a single individual.

Examples Feature Extraction

In an automated sleep staging system features are extracted from each 30s. Sample data was collected from a database composed of overnightrecordings from 37 subjects. RR-Interval Series Features: To calculate apower spectral density estimate, the data (RR_(norm) intervals fallingwithin the epoch) from the epoch is zero-meaned, windowed (using aHanning window), and the square of its Discrete Fourier Transform (DFT)is taken as a single periodogram estimate of the interval based powerspectral density. The x-ordinate of this estimate is in cycles/interval,which can be converted to cycles/second by dividing by the mean RR forthe epoch. From this spectral estimate, five features are calculated:

-   -   the logarithm of the normalized LF (power in the 0.05-0.15 Hz        band),    -   the logarithm of the normalized HF (power in the 0.15-0.5 Hz        band), where normalization is achieved by dividing by the total        power in the VLF, LF, and HF bands (0.01-0.5 Hz),    -   the LF/HF power ratio    -   the mean respiratory frequency, which is defined by finding the        frequency of maximum power in the HF band, and    -   the logarithm of the power at the mean respiratory frequency.

In addition to the RR spectral features, we also used a range oftemporal RR features for each 30 s epoch. These features were:

-   -   the mean RR_(norm),    -   the standard deviation of RR_(norm),    -   the difference between the longest and shortest RR_(norm)        interval in the epoch, and    -   the mean value of the RR_(detrend) in the epoch.

The difference between longest and shortest RR_(norm) within the epochis an attempt to quantify some of the dynamic behavior within the epoch(perhaps waking epochs are more dynamic than sleep.) The meanRR_(detrend) in one epoch is an attempt to examine the short-timevariation in the RR interval series. Since each RR_(detrend) value is ameasure of the present RR_(norm) relative to the previous 15 minutes ofRR_(norm), the mean RR_(detrend) of an epoch is a measure of whether theheart rate in the present epoch is less than or greater than it has beenover the last 15 minutes. This allows the discrimination of sudden risesin the heart rate, indicating short arousals, which may not risesignificantly above the heart rate of other epochs of sleep.

ECG Derived Respiratory Features: The EDR epoch is taken as the EDRpoints corresponding to the R peaks falling within the epoch. Thespectrum is calculated as for the RR interval series. From the EDRspectrum, the VLF (0.01-0.05 Hz), LF (0.05-0.15 Hz), HF (0.15-0.5 Hz)powers, respiratory frequency, and the power at respiratory frequencyare estimated. The standard deviation of each epoch's EDR was alsocalculated.

RR-EDR Cross-Spectral Features: The VLF (0.01-0.05 Hz), LF (0.05-0.15Hz), HF (0.15-0.5 Hz) powers were calculated from the cross-spectrum ofthe RR interval series and EDR for each epoch.

Ribcage Respiratory Effort Features: As with the RR interval series andthe EDR, we calculate the ribcage respiratory effort spectrum as thesquare of the DFT of the ribcage respiratory effort signal for thatepoch, windowed with a Hanning window. From the spectrum we calculatethe logarithm of the power in the 3 bands—VLF (0.01-0.05 Hz), LF(0.05-0.15 Hz) and HF (0.15-0.5 Hz). The definition of these bands istaken directly from the corresponding definitions for ECG signals.Furthermore we estimate the respiratory frequency as the frequency ofpeak power in the range of 0.05 Hz-0.5 Hz, and also the logarithm of thepower at that frequency. In the following table features 1-9 are derivedfrom the ECG, and include both time and frequency domain heart beatfeatures. The features 10-15 are also derived from the ECG signals,however, in this case they are derived from the amplitude modulation ofthe ECG caused by the respiration. The features 16-18 are derived fromthe preceding features. It should be noted that the steps of FIG. 1 arenot necessarily carried out in the order indicated. The time domainfeatures may be extracted before the frequency domain features or inparallel. Also, as shown below for the features 16-18 any of the steps6, 7, 21 22, 31, or 32 may include additional feature extraction basedon previously-extracted features. Finally, the features 19-27 of Table Ibelow are extracted in steps 31 and 32.

TABLE I FEATURE LIST 1 RR LF band (frequency domain) RR Interval 2 RR HFband (frequency domain) based 3 RR standard deviation (time domain)features 4 RR respiratory freq (frequency domain) 5 RR respiratory power(frequency domain) 6 LF/HF Ratio (frequency domain) 7 Longest ShortestRR difference (time domain) 8 Detrended RR mean (time domain) 9 RR mean(time domain) 10 EDR VLF band (frequency domain) EDR based 11 EDR LFband (frequency domain) features 12 EDR HF band (frequency domain) 13EDR standard deviation (time domain) 14 EDR respiratory frequency(frequency domain) 15 EDR respiratory power (frequency domain) 16 RR-EDRCross spectrum VLF band (freq. dom.) EDR-RR 17 RR-EDR Cross spectrum LFband (freq. dom.) Interval based 18 RR-EDR Cross spectrum HF band (freq.dom.) features 19 Ribcage Respiratory effort VLF band (freq. dom.)Ribcage effort 20 Ribcage Respiratory effort LF band (freq. dom.) basedfeatures 21 Ribcage Respiratory effort HF band (freq. dom.) 22 RibcageRespiratory effort respiratory frequency (freq. dom.) 23 RibcageRespiratory effort respiratory power (freq. dom.) 24 Envelope Power(time domain) 25 Breath by breath correlation (time domain) 26 Breathlength variation (time domain) 27 Time domain respiratory frequency(time domain)

As set out above we derive several time domain features from the ribcagerespiratory effort signal. The first is an estimate of its envelopepower. We find the standard deviation of the peak values for the epoch,and similarly the standard deviation of the troughs. We then find themean of the two values and divide by the standard deviation of theribcage respiratory effort signal for the epoch. Essentially we aremeasuring the average top and bottom envelope powers as a fraction ofthe total signal power for the epoch. We denote this feature “EnvelopePower”. The second time domain feature attempts to measure abreath-by-breath correlation. We define a breath cycle as the time fromthe trough of one breath to the trough of the next. We find thecross-correlation of the adjacent breaths. Clearly, in most cases thebreaths will be of different lengths, in this case the shorter is paddedwith zeros to make it of equal length. We find the maximumcross-correlation value and divide it by the maximum of the energy ofeither breath alone to normalize the maximum cross-correlation value.The maximum cross-correlation values, for all pairs of adjacent breathsin the epoch, are then averaged. We denote this feature“Breath-by-Breath Correlation”. The third time domain feature is afurther measure of breath-by-breath variation. We take the standarddeviation of the time between peak locations, similarly we take thestandard deviation of the time between trough locations. We then takethe mean of these two deviations. We denote this “Breath LengthVariation”. Finally we derive a second estimate of the respiratoryfrequency, using non-spectral means. We calculate the mean time betweenadjacent peaks and between adjacent troughs. The frequency ofrespiration is calculated as the inverse of this time. We denote thisfeature “Time Domain Respiratory Frequency”. All estimates ofrespiratory frequency were further normalized by subtracting (from eachepoch's estimate of the frequency) the median value of that parameterover all epochs for the entire night. This was deemed a necessary stepas the mean respiratory frequency will vary from subject to subject. Themedian was subtracted as it is more robust than the mean to outliers.

The complete list of features for each 30 s epoch is given in Table I,and we will use the indices from this table in referring to possiblefeature combinations.

Classifier Model Quadratic Discriminant Classifier

Following the feature extraction stage described above, each 30 s epochnow has an associated set of 27 features-9 RR-based, 6 EDR-based, 3cross-spectral-based and 9 ribcage respiratory effort based. Theclassifier is a quadratic discriminant classifier (QDC), based on Bayes'rule. In deriving a decision rule for a QDC, gaussianity of the featurevector distributions, and independence between successive epochs istheoretically assumed. Neither gaussianity nor independence willnecessarily be satisfied. In deriving the features above, we haveattempted to ensure that each feature has an approximately Gaussiandistribution. This can be ensured, for example, by using the logarithmof the spectral powers, rather than their absolute values.Classification accuracy may be improved if the dependence between epochsis considered as a post-processing step.

A quadratic discriminant classifier is derived as follows. Let ω_(i)signify the ith class. In this application there are three classes, S,W, and R. Let x denote the feature vector corresponding to a certainepoch. The feature vector in this case contains at most 27 elements,which are a selection of the features of Table I. Using Bayes' rule wewish to find the class i which will maximize the posterior probability:

$\begin{matrix}{{P\left( {\omega_{i}❘x} \right)} = \frac{{P\left( \omega_{i} \right)}{p\left( {x❘\omega_{i}} \right)}}{p(x)}} & (1)\end{matrix}$

Maximizing the left hand side of (1) is equivalent to maximizing itslogarithm. Therefore, assuming a normal distribution for the featurevector, p(x|ω_(i)) becomes:

$\begin{matrix}{{p\left( {x❘\omega_{i}} \right)} = {\frac{1}{\left( {2\pi} \right)^{d\; 12}{\sum\limits_{i}^{\;}}^{1/2}}{\exp\left\lbrack {{- \frac{1}{2_{0}}}\left( {x - \mu_{i}} \right)^{T}{\sum_{i}^{- 1}\left( {x - \mu_{i}} \right)}} \right\rbrack}}} & (2)\end{matrix}$

where Σ_(i) is the covariance matrix of the ith class, and μ_(i) is themean vector of the ith class. Substituting (2) into the naturallogarithm of (1), our problem is transformed into finding the class iwhich maximizes the discriminant value g_(i(x)) for a given test featurevector x:

$\begin{matrix}{{{g_{i}(x)} = {{x^{T}W_{i}x} + {w_{i}x} + k_{i}}}{{where}\text{:}}{{w_{i} = {{- \frac{1}{2}}\sum_{i}^{- 1}}},{w_{i} = {\sum_{i}^{- 1}\mu_{i}}}}{k_{i} = {{{- \frac{1}{2}}\mu_{i}{\sum_{i}^{- 1}\mu_{i}}} - {\frac{1}{2}1n{\sum_{i}}} + {1n\;{P\left( \omega_{i} \right)}}}}} & (3)\end{matrix}$

The class with the highest discriminant value is chosen as the assignedclass for that feature vector. To construct the quadratic discriminantclassifier, therefore, we estimate the covariance matrix and mean forthe features corresponding to each class, and also the prior probabilityof the class occurring.

It will be appreciated by one skilled in the art that a differentclassifier such as a linear discriminant classifier, a logisticdiscriminant classifier, a neural network, or a k-means clusteringclassifier could be used.

Feature Subset Selection

In theory, with quadratic or linear discriminant classifiers, theaddition of features containing little or no relevant information in theclassification process will not degrade the performance of theclassifier. One could include all features in the classification processand features containing no information will be “ignored” by theclassifier. In practice this is rarely true—null features add “noise” tothe system, and the removal of these redundant features can greatlyimprove results. However, with 27 features to choose from, we areallowed 2²⁷ feature subset combinations, so it not feasible to searchall possible combinations. The classifier includes a process whichallows efficient searching of the feature subset combinations.

A sequential forward floating search (SFFS) process identifies thefeature subset that will optimize the classification performance. Thek-coefficient is a measure of inter-rater agreement and takes intoaccount the prior probability of a specific class occurring. The tworaters under comparison are our sleep staging system and an expertpolysomnograph annotator.

An SFFS algorithm operates as follows. Three passes are made with theordinary sequential forward selection (SFS), so that three features areselected. One pass of the SFS simply adds the feature that most improvesperformance to the already selected features. Next, “unselection” of aselected feature is considered. The feature is found which most improvesperformance by its removal, and it is unselected. However, if noimprovement is seen by the removal of any features then no features areunselected. Following the unselection phase the SFS is run again toselect another feature. The cycle of a selection phase (with the SFS),followed by a possible unselection phase, is repeated until either thenumber of features required is reached, or until the SFS phase fails toselect a feature immediately followed by the failure of the unselectionphase to remove a feature, in which case it is impossible for theselected feature subset to change and the algorithm must terminate.

The advantage of the SFFS over the SFS, or other greedy featureselection algorithms, is its ability to avoid nesting. Nesting occurs ingreedy selection algorithms if a feature is selected early on that isnot a member of the optimal feature subset, as it cannot be removed.Another algorithm, the plus l, takeaway r algorithm, can also avoidnesting. Its operation is similar to the SFFS and it provides similarresults but has a longer execution time as it always removes l features,whereas the SFFS judiciously decides whether to remove a feature or not.Indeed the SFFS may not find the optimal feature subset, as it isinherently a sub-optimal search, but will often yield results comparablewith those of an exhaustive search.

The quadratic discriminant classifier model is used to discriminatebetween the three classes W, R, and S for a single subject's recording.To train the classifier (i.e., estimate class prior probabilities,covariance matrices, and means) 20% of the epochs for that night arerandomly selected. Before the training data is chosen the priorprobabilities for each of the three stages occurring are estimated usingall 37 subjects. These probabilities are calculated as: P(W)=0.26,P(R)=0.13, P(S)=0.61. The training data is chosen in such a way that theratios of each class are in the proportion of the prior probabilitieswhere possible. However, if the covariance matrix of a class isestimated using as many (or less) observations than there are features,the matrix will be singular, prohibiting the use of discriminantanalysis. In such cases the class containing insufficient observationsis simply eliminated from the training data. To test the system theremaining 80% of the subject's epochs are presented to the classifier.

There are several means for assessing the performance of the system,including the overall accuracy (the percentage of correctly classifiedepochs from the test set), the absolute error from the true sleepefficiency, and Cohen's kappa statistic k. A k value above 0.7 istypically taken to indicate a high-degree of inter-system reliability.The accuracies and k obtained for each of the 37 subjects are averagedto give a mean accuracy and k. Each subject's accuracy and k. is itselfderived from an ensemble of ten classifier runs, with differingselections of training data each time. The accuracies are derived froman ensemble average so as to remove any bias caused by the randomselection of the training data.

Design of a Subject Independent Classifier

To construct a subject independent classifier, features from the other36 subjects were pooled together to form the training data for theclassifier, again training a 3-class—W, R, and S—classifier byestimating the class prior probabilities, covariance matrices, andmeans. This was repeated 37 times, leaving one subject out of thetraining data each time. In each case the remaining subject was used totest the system. Obtained accuracies and k from each of the 37 runs areaveraged for an overall estimate of performance.

Design of an EEG Comparative Classifier

To gain a perspective on the results of the subject specific and subjectindependent systems, two further systems were designed using spectraland time domain features from the EEG in place of the cardiorespiratoryfeatures described. The EEG spectral features used are: average power inthe delta (0.75-3.75 Hz), theta (4-7.75 Hz), alpha (8-12 Hz), spindle(12.25-15 Hz), and beta (15.25-30 Hz) frequency bands.

The powers in the designated frequency bands were calculated using aperiodogram estimator. The 30-second EEG epoch was windowed using asliding 2-second Hanning window with a 1-second overlap into 29segments. The periodogram was constructed by averaging the square of theDFT of each segment over all 29 segments. The relevant frequency bandswere then integrated to give the resulting band power.

The time domain features were the Hjorth parameters of activity,mobility and complexity. They were derived from the entire 30-secondepoch. Letting x denote the EEG epoch containing N samples, the Hjorthparameters are defined as:

${{Activity}(x)} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}\left( {x_{i} - \mu_{x}} \right)^{2}}}$${{Mobility}(x)} = \sqrt{\frac{\sigma\left( x^{\prime} \right)}{\sigma(x)}}$${{Complexity}(x)} = \frac{{Mobility}\left( x^{\prime} \right)}{{Mobility}(x)}$

where x′ is the first derivative of x, σ(x) is the standard deviation ofx, and μ_(x) is the mean of x. We also note that the activity is equalto the variance of x.

Using the same training and classifier paradigm as outlined above, thesubject-specific and subject-independent classifiers were designed andtested.

Results Subject Specific Results

Table II details the results for all subjects, and for subjects brokendown by low and high AHI indices, after presenting all 27 features tothe classifier.

TABLE II SUBJECT SPECIFIC RESULTS Subject Mean Cohen's Average SleepGroup Kappa Coefficient κ Mean Accuracy Efficiency Error All 0.56 ± 0.1179% ± 5.4% 3.3% Low AHI 0.6 ± 0.1 81% ± 4.6% 2.5% High AHI 0.51 ± 0.0977% ± 5.5% 4%

In Table III we list the features selected by the SFFS classifier. Theindices listed refer to the feature list defined in Table I.

TABLE III SUBJECT SPECIFIC FEATURES Subject group Features Selected (inorder of selection) All 9, 27, 21, 8, 18, 20, 2, 16 Low AHI 22, 9, 23,8, 15, 19, 2, 16 High AHI 8, 2, 9, 23, 27, 20

Subject Independent Results

In Table IV we present the results for all subjects after presenting all27 features to the classifier.

TABLE IV SUBJECT INDEPENDENT RESULTS Subject Mean Cohen's Average SleepGroup Kappa Coefficient κ Mean Accuracy Efficiency Error All 0.32 ± 0.1167% ± 7.8% 11% Low AHI 0.33 ± 0.1  68% ± 7.3% 11.5% High AHI 0.31 ± 0.0869% ± 7%   10%

Table V below lists the features selected by the features selectionalgorithm in the Subject Independent case.

TABLE V SUBJECT INDEPENDENT FEATURES Subject group Features Selected (inorder of selection) All 22, 8, 20, 2, 4, 23, 5, 25, 9, 27, 21, 1, 11,16, 17 Low AHI 19, 25, 4, 5, 8, 9, 16, 22, 12, 15, 11, 23 High AHI 8,22, 2, 23, 25, 20, 14, 4, 16, 15

Low AHI Versus High AHI

We wish to investigate the difference in performance between subjectswith low apnea-hypopnea indices (AHI) and those with high AHIs. Werepeat the above-mentioned Subject Specific and Subject Independentexperiments with the subjects split into low AHIs (<10 apneas orhypopneas per hour) and high AHIs. There were 14 subjects with high AHIsthe mean AHI was 26 and the standard deviation was 19.8. The remaining23 subjects with low AHIs had a mean AHI of 3.4 and a standard deviationof 2.2.

Comparative EEG Results

Tables VI and VII summarize the results of the Subject Specific andSubject Independent systems when trained using the 8 EEG featuresdescribed earlier (no feature selection algorithm was used). As for thecardio-respiratory scoring system, we provide results broken down byhigh and low AHI class.

TABLE VI EEG SUBJECT SPECIFIC RESULTS Subject Mean Cohen's Average SleepGroup Kappa Coefficient κ Mean Accuracy Efficiency Error All 0.75 ± 0.1287% ± 6.8% 2.7% Low AHI 0.76 ± 0.12 87% ± 7.4% 3% High AHI 0.73 ± 0.1 87% ± 5.8% 2.2%

TABLE VII EEG SUBJECT INDEPENDENT RESULTS Subject Mean Cohen's AverageSleep Group Kappa Coefficient κ Mean Accuracy Efficiency Error All 0.68± 0.15 84% ± 8%   6.4% Low AHI  0.7 ± 0.16 84% ± 9.4% 7.9% High AHI 0.68± 0.13 84% ± 7.7% 5%

There may be a delay inherent in some inductance plethysmograph devices.Although there was no delay associated with the device used in thisstudy, some methods may contain a delay in recording, relative to theECG, of 2 or 3 seconds. However, even when such a delay exists it isinsignificant since we are using a 30 second epoch, and since only ourinterpretation of transitional epochs (epochs on the boundary of a sleepstate change) will be affected by such a delay.

It will be appreciated that the invention provides for comprehensiveanalysis of sleep stages arising from the richness of the dataincorporated in the features and the manner in which they are combinedin the classifier. The classifier achieves effectively the same qualityof output as a system which uses brain activity sensor inputs because itcan be trained using such sensor inputs. Also, because of use ofdifferent threads, both cardiac and physical respiration (ribcage)threads, and cross-coupling of the features there is excellentrobustness.

The invention is not limited to the embodiments described but may bevaried in construction and detail. For example, a classifier other thanthat described above may be used.

The invention claimed is:
 1. A sleep monitoring method performed by asystem comprising a sensor interface and one or more processors, themethod comprising the steps of: receiving, at the sensor interface,signals from a first sensor monitoring a cardiac activity of a patient;deriving, by the one or more processors, heart beat interval data fromthe signals received from the first sensor; extracting, by the one ormore processors, one or more first time domain features and one or morefirst frequency domain features from the heart beat interval data;deriving, by the one or more processors, amplitude modulation data,caused by respiratory motion, from the signals received from the firstsensor; extracting, by the one or more processors, one or more secondtime domain features and one or more second frequency domain featuresfrom the amplitude modulation data; combining, by the one or moreprocessors operating as a classifier, one or more of the first extractedtime domain features, one or more of the first extracted frequencydomain features, one or more of the second extracted time domainfeatures, and one or more of the second extracted frequency domainfeatures; determining, by the one or more processors operating as theclassifier, a plurality of sleep or wake stages of the patient based onthe combined features, wherein the plurality of sleep or wake stagesinclude one or more of (a) Wake stages, (b) Rapid Eye Movement (REM)Sleep stages, and (c) Non-REM Sleep stages; and generating, by the oneor more processors, a plot indicating periods of the determinedplurality of sleep or wake stages.
 2. A computer program productcomprising software code stored on non-transitory computer readablemedia which, when executed by a digital processor, performs the steps ofthe method of claim
 1. 3. A sleep monitoring method as claimed in claim1, wherein the determining is further based on probability distributionsof the combined features.
 4. A sleep monitoring method as claimed inclaim 1, further comprising: extracting, by the one or more processors,one or more third frequency domain features from (a) one or more of thefirst extracted time domain features, (b) one or more of the firstextracted frequency domain features, (c) one or more of the secondextracted time domain features, or (d) one or more of the secondextracted frequency domain features.
 5. A sleep monitoring method asclaimed in claim 4, wherein the one or more third frequency domainfeatures includes (a) a power in a Very Low Frequency (VLF) band of across-spectrum of a series of RR intervals and a series of R-peakamplitudes, (b) a power in a Low Frequency (LF) band of a cross-spectrumof a series of RR intervals and a series of R-peak amplitudes, or (c) apower in a High Frequency (HF) band of a cross-spectrum of a series ofRR intervals and a series of R-peak amplitudes.
 6. A sleep monitoringmethod as claimed in claim 1, further comprising: receiving, at thesensor interface, signals from a second sensor monitoring respiration ofa patient; deriving, by the one or more processors, direct respirationdata from the signals received from the second sensor; and extracting,by the one or more processors, one or more third time domain featuresand one or more third frequency domain features from the directrespiration data, wherein the combining further comprises combining oneor more of the third time domain features and one or more of the thirdfrequency domain features.
 7. A sleep monitoring method as claimed inclaim 6, wherein the one or more third time domain features includes (a)an envelope power, (b) a breath-by-breath correlation, (c) a breathlength variation, or (d) a time domain respiratory frequency.
 8. A sleepmonitoring method as claimed in claim 6, wherein the one or more thirdfrequency domain features includes (a) a power in a Very Low Frequency(VLF) band of a ribcage respiratory effort signal, (b) a power in a LowFrequency (LF) band of a ribcage respiratory effort signal, or (c) apower in a High Frequency (HF) band of a ribcage respiratory effortsignal.
 9. A sleep monitoring method as claimed in claim 8, wherein (a)the VLF band ranges from 0.01 Hz to 0.05 Hz, (b) the LF band ranges from0.05 Hz to 0.15 Hz, and (c) the HF band ranges from 0.15 Hz to 0.5 Hz.10. A sleep monitoring method as claimed in claim 8, wherein the one ormore third frequency domain features further includes (a) a third typeof a mean respiratory frequency, which is defined by finding a frequencyof maximum power in an HF band of a ribcage respiratory effort signal or(b) a power at a third type of a mean respiratory frequency.
 11. A sleepmonitoring method as claimed in claim 6, wherein the first sensorcomprises an electrocardiogram (ECG) device and the second sensorcomprises a plethysmogram device.
 12. A sleep monitoring method asclaimed in claim 1, wherein the one or more first time domain featuresincludes (a) a mean of normalized RR intervals in an epoch, (b) astandard deviation of normalized RR intervals in an epoch, (c) adifference between a longest and a shortest normalized RR interval in anepoch, or (d) a mean of detrended RR intervals in an epoch.
 13. A sleepmonitoring method as claimed in claim 1, wherein the one or more secondtime domain features includes a standard deviation of R-peak amplitudesin an epoch.
 14. A sleep monitoring method as claimed in claim 1,wherein the one or more first frequency domain features includes (a) apower in a Low Frequency (LF) band of a series of RR intervals, (b) apower in a High Frequency (HF) band of a series of RR intervals, or (c)an LF/HF power ratio.
 15. A sleep monitoring method as claimed in claim14, wherein (a) the LF band ranges from 0.05 Hz to 0.15 Hz and (b) theHF band ranges from 0.15 Hz to 0.5 Hz.
 16. A sleep monitoring method asclaimed in claim 14, wherein the one or more first frequency domainfeatures further includes (a) a first type of a mean respiratoryfrequency, which is defined by finding a frequency of maximum power inan HF band of a series of RR intervals or (b) a power at a first type ofa mean respiratory frequency.
 17. A sleep monitoring method as claimedin claim 1, wherein the one or more second frequency domain featuresincludes (a) a power in a Very Low Frequency (VLF) band of a series ofR-peak amplitudes, (b) a power in a Low Frequency (LF) band of a seriesof R-peak amplitudes, or (c) a power in a High Frequency (HF) band of aseries of R-peak amplitudes.
 18. A sleep monitoring method as claimed inclaim 17, wherein (a) the VLF band ranges from 0.01 Hz to 0.05 Hz, (b)the LF band ranges from 0.05 Hz to 0.15 Hz, and (c) the HF band rangesfrom 0.15 Hz to 0.5 Hz.
 19. A sleep monitoring method as claimed inclaim 17, wherein the one or more second frequency domain featuresfurther includes (a) a second type of a mean respiratory frequency,which is defined by finding a frequency of maximum power in an HF bandof a series of R-peak amplitudes or (b) a power at a second type of amean respiratory frequency.
 20. A sleep monitoring method as claimed inclaim 1, wherein the first sensor comprises an electrocardiogram (ECG)device.